With the advent of general access computer networks, such as the Internet, people may now easily exchange application data between computer systems. Unfortunately, some people have taken advantage of such easy data exchange by proliferating unwanted data. Non-exhaustive examples of unwanted data include unwanted electronic messages (i.e. SPAM, etc.), pornographic material, etc.
In the context of unwanted electronic messages, traditional algorithms have used word groupings and/or electronic message header information in combination with various Bayesian logic to drive a determination as to whether an electronic message is unwanted. Unfortunately, generators of unwanted electronic messages have developed techniques for overcoming such technology.
For example, legitimate-appearing text is sometimes included in the electronic message in a near white color on a white background. Further, dynamic hypertext markup language (DHTML) is used to place an unwanted message on top of such legitimate-appearing text in a readable color. To this end, the legitimate-appearing text serves to distract or circumvent the aforementioned detection technology. Still yet, the unwanted message may not even include text, and instead include a picture which is difficult to detect.
Another example of technology developed to circumvent unwanted data detectors involves the use of small words in a small font to “draw” the unwanted message in text. See, for example, Table 1 below.
TABLE 1hihihowhi hihi Ihihihow
While the example in Table 1 is simplified, it is readily apparent that the actual unwanted message can only be read by a human and is thus difficult to detect by automated mechanisms.
In the foregoing cases, an unwanted data detector is unfortunately limited to blocking based on email header information, etc. There is thus a need for overcoming these and/or other problems associated with the prior art.